Overview

Dataset statistics

Number of variables49
Number of observations483
Missing cells681
Missing cells (%)2.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory185.0 KiB
Average record size in memory392.3 B

Variable types

Numeric9
Categorical39
DateTime1

Warnings

GR_POD has constant value "1" Constant
ENTE has a high cardinality: 480 distinct values High cardinality
POP is highly correlated with TAM.ATV and 1 other fieldsHigh correlation
TAM.ATV is highly correlated with POP and 1 other fieldsHigh correlation
GR_C.ÉT is highly correlated with CIE_REL and 3 other fieldsHigh correlation
CIE_REL is highly correlated with GR_C.ÉT and 2 other fieldsHigh correlation
GI_COMITÊ is highly correlated with LI_DIR.CERTHigh correlation
GTI_POL.INFR. is highly correlated with GR_C.ÉT and 1 other fieldsHigh correlation
RP_REL.GOV is highly correlated with GR_C.ÉT and 2 other fieldsHigh correlation
LI_DIR.CERT is highly correlated with GI_COMITÊHigh correlation
LI_P.EST. is highly correlated with GR_C.ÉT and 3 other fieldsHigh correlation
R.FIN is highly correlated with R.ATHigh correlation
IND.SF is highly correlated with IND.COBHigh correlation
R.INV is highly correlated with TAM.ATVHigh correlation
R.AT is highly correlated with POP and 1 other fieldsHigh correlation
IND.COB is highly correlated with IND.SFHigh correlation
POP is highly correlated with TAM.ATV and 2 other fieldsHigh correlation
TAM.ATV is highly correlated with POP and 2 other fieldsHigh correlation
GR_C.ÉT is highly correlated with CIE_REL and 3 other fieldsHigh correlation
CIE_REL is highly correlated with GR_C.ÉT and 2 other fieldsHigh correlation
GI_COMITÊ is highly correlated with LI_DIR.CERTHigh correlation
GTI_POL.INFR. is highly correlated with GR_C.ÉT and 1 other fieldsHigh correlation
RP_REL.GOV is highly correlated with GR_C.ÉT and 2 other fieldsHigh correlation
LI_DIR.CERT is highly correlated with GI_COMITÊHigh correlation
LI_P.EST. is highly correlated with GR_C.ÉT and 3 other fieldsHigh correlation
R.FIN is highly correlated with IND.SF and 1 other fieldsHigh correlation
IND.SF is highly correlated with R.FIN and 1 other fieldsHigh correlation
R.INV is highly correlated with POP and 2 other fieldsHigh correlation
R.AT is highly correlated with POP and 3 other fieldsHigh correlation
IND.COB is highly correlated with R.FIN and 2 other fieldsHigh correlation
POP is highly correlated with R.ATHigh correlation
TAM.ATV is highly correlated with R.INVHigh correlation
GR_C.ÉT is highly correlated with CIE_REL and 3 other fieldsHigh correlation
CIE_REL is highly correlated with GR_C.ÉT and 2 other fieldsHigh correlation
GI_COMITÊ is highly correlated with LI_DIR.CERTHigh correlation
GTI_POL.INFR. is highly correlated with GR_C.ÉT and 1 other fieldsHigh correlation
RP_REL.GOV is highly correlated with GR_C.ÉT and 2 other fieldsHigh correlation
LI_DIR.CERT is highly correlated with GI_COMITÊHigh correlation
LI_P.EST. is highly correlated with GR_C.ÉT and 3 other fieldsHigh correlation
R.FIN is highly correlated with IND.SFHigh correlation
IND.SF is highly correlated with R.FINHigh correlation
R.INV is highly correlated with TAM.ATVHigh correlation
R.AT is highly correlated with POPHigh correlation
RP_REG.ENV is highly correlated with GI_C.TÉC and 4 other fieldsHigh correlation
GI_C.TÉC is highly correlated with RP_REG.ENV and 3 other fieldsHigh correlation
UF is highly correlated with RP_REG.ENV and 10 other fieldsHigh correlation
PS_GEST is highly correlated with UFHigh correlation
R.FIN is highly correlated with R.INV and 3 other fieldsHigh correlation
R.INV is highly correlated with UF and 4 other fieldsHigh correlation
CIE_FISC is highly correlated with GI_DIV.CART and 3 other fieldsHigh correlation
GI_COMITÊ is highly correlated with GI_C.TÉC and 1 other fieldsHigh correlation
VAR.INV is highly correlated with R.ATHigh correlation
AD.PROG is highly correlated with High correlation
C.PROG is highly correlated with UF and 4 other fieldsHigh correlation
GI_DIV.CART is highly correlated with CIE_FISC and 3 other fieldsHigh correlation
DDM_CART is highly correlated with High correlation
RP_SITE is highly correlated with N.JUR and 1 other fieldsHigh correlation
POP is highly correlated with R.INV and 4 other fieldsHigh correlation
REGIÃO is highly correlated with UFHigh correlation
CIE_REL is highly correlated with and 5 other fieldsHigh correlation
SA_PUB.REL is highly correlated with RP_REG.ENV and 6 other fieldsHigh correlation
R.AT is highly correlated with R.FIN and 5 other fieldsHigh correlation
is highly correlated with AD.PROG and 7 other fieldsHigh correlation
DDM_ATAS is highly correlated with CIE_REL and 4 other fieldsHigh correlation
TAM.ATV is highly correlated with UF and 6 other fieldsHigh correlation
SA_REPAS is highly correlated with RP_REG.ENV and 4 other fieldsHigh correlation
N.JUR is highly correlated with UF and 1 other fieldsHigh correlation
GTI_INV.TI is highly correlated with High correlation
MassaSeg. is highly correlated with UF and 5 other fieldsHigh correlation
PRT_massa is highly correlated with UF and 5 other fieldsHigh correlation
GTI_POL.INFR. is highly correlated with CIE_REL and 3 other fieldsHigh correlation
LI_DIR.CERT is highly correlated with GI_COMITÊHigh correlation
CIE_CRP is highly correlated with RP_REG.ENV and 6 other fieldsHigh correlation
GI_P.INV is highly correlated with C.PROGHigh correlation
LI_P.EST. is highly correlated with C.PROG and 5 other fieldsHigh correlation
GR_C.ÉT is highly correlated with C.PROG and 6 other fieldsHigh correlation
PS_OUV is highly correlated with RP_SITE and 1 other fieldsHigh correlation
RP_REL.GOV is highly correlated with CIE_REL and 3 other fieldsHigh correlation
CIE_AUD is highly correlated with POPHigh correlation
RP_REG.ENV is highly correlated with GR_PODHigh correlation
GI_C.TÉC is highly correlated with GI_COMITÊ and 2 other fieldsHigh correlation
UF is highly correlated with PS_GEST and 2 other fieldsHigh correlation
PS_GEST is highly correlated with UF and 1 other fieldsHigh correlation
CIE_FISC is highly correlated with GR_PODHigh correlation
GI_COMITÊ is highly correlated with GI_C.TÉC and 2 other fieldsHigh correlation
GR_SEGR is highly correlated with GR_PODHigh correlation
AD.PROG is highly correlated with GR_C.ÉT and 1 other fieldsHigh correlation
C.PROG is highly correlated with DDM_ACES and 4 other fieldsHigh correlation
GI_DIV.CART is highly correlated with SA_REPAS and 1 other fieldsHigh correlation
SA_AMRTZ is highly correlated with GR_PODHigh correlation
DDM_CART is highly correlated with GR_PODHigh correlation
RP_SITE is highly correlated with GR_PODHigh correlation
REGIÃO is highly correlated with UF and 1 other fieldsHigh correlation
CIE_REL is highly correlated with LI_P.EST. and 3 other fieldsHigh correlation
SA_PUB.REL is highly correlated with CIE_CRP and 1 other fieldsHigh correlation
DDM_ATAS is highly correlated with GR_PODHigh correlation
SA_REPAS is highly correlated with GI_DIV.CART and 1 other fieldsHigh correlation
N.JUR is highly correlated with GR_PODHigh correlation
IP_SELEC is highly correlated with GR_PODHigh correlation
GTI_INV.TI is highly correlated with GR_PODHigh correlation
DDM_ACES is highly correlated with C.PROG and 1 other fieldsHigh correlation
MassaSeg. is highly correlated with PRT_massa and 1 other fieldsHigh correlation
PRT_massa is highly correlated with MassaSeg. and 1 other fieldsHigh correlation
GTI_POL.INFR. is highly correlated with LI_P.EST. and 2 other fieldsHigh correlation
LI_DIR.CERT is highly correlated with GI_C.TÉC and 2 other fieldsHigh correlation
CIE_CRP is highly correlated with SA_PUB.REL and 1 other fieldsHigh correlation
GI_P.INV is highly correlated with C.PROG and 1 other fieldsHigh correlation
IP_REP.CF is highly correlated with GR_PODHigh correlation
SA_R.ALIQ is highly correlated with GR_PODHigh correlation
LI_P.EST. is highly correlated with C.PROG and 5 other fieldsHigh correlation
GR_C.ÉT is highly correlated with AD.PROG and 6 other fieldsHigh correlation
PS_OUV is highly correlated with GR_PODHigh correlation
GI_CONS is highly correlated with GR_PODHigh correlation
RP_REL.GOV is highly correlated with CIE_REL and 3 other fieldsHigh correlation
LI_CERT.MB is highly correlated with GR_PODHigh correlation
CIE_AUD is highly correlated with GR_PODHigh correlation
GR_POD is highly correlated with RP_REG.ENV and 36 other fieldsHigh correlation
N.JUR has 10 (2.1%) missing values Missing
POP has 27 (5.6%) missing values Missing
TAM.ATV has 24 (5.0%) missing values Missing
C.PROG has 283 (58.6%) missing values Missing
GI_CONS has 12 (2.5%) missing values Missing
SA_REPAS has 5 (1.0%) missing values Missing
DDM_ATAS has 5 (1.0%) missing values Missing
RP_REL.GOV has 5 (1.0%) missing values Missing
IP_REP.CF has 16 (3.3%) missing values Missing
LI_CERT.MB has 16 (3.3%) missing values Missing
R.FIN has 30 (6.2%) missing values Missing
IND.SF has 29 (6.0%) missing values Missing
R.INV has 23 (4.8%) missing values Missing
VAR.INV has 24 (5.0%) missing values Missing
R.AT has 70 (14.5%) missing values Missing
IND.COB has 70 (14.5%) missing values Missing
is uniformly distributed Uniform
ENTE is uniformly distributed Uniform
has unique values Unique
IND.SF has 6 (1.2%) zeros Zeros

Reproduction

Analysis started2021-05-25 22:21:37.221714
Analysis finished2021-05-25 22:22:56.970360
Duration1 minute and 19.75 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables


Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct483
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean242
Minimum1
Maximum483
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2021-05-25T18:22:57.271555image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25.1
Q1121.5
median242
Q3362.5
95-th percentile458.9
Maximum483
Range482
Interquartile range (IQR)241

Descriptive statistics

Standard deviation139.5743529
Coefficient of variation (CV)0.5767535246
Kurtosis-1.2
Mean242
Median Absolute Deviation (MAD)121
Skewness0
Sum116886
Variance19481
MonotonicityStrictly increasing
2021-05-25T18:22:57.662279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.2%
3631
 
0.2%
3311
 
0.2%
3301
 
0.2%
3291
 
0.2%
3281
 
0.2%
3271
 
0.2%
3261
 
0.2%
3251
 
0.2%
3241
 
0.2%
Other values (473)473
97.9%
ValueCountFrequency (%)
11
0.2%
21
0.2%
31
0.2%
41
0.2%
51
0.2%
61
0.2%
71
0.2%
81
0.2%
91
0.2%
101
0.2%
ValueCountFrequency (%)
4831
0.2%
4821
0.2%
4811
0.2%
4801
0.2%
4791
0.2%
4781
0.2%
4771
0.2%
4761
0.2%
4751
0.2%
4741
0.2%

REGIÃO
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
SE
178 
S
106 
NE
100 
CO
65 
N
34 

Length

Max length2
Median length2
Mean length1.710144928
Min length1

Characters and Unicode

Total characters826
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCO
2nd rowCO
3rd rowNE
4th rowNE
5th rowCO

Common Values

ValueCountFrequency (%)
SE178
36.9%
S106
21.9%
NE100
20.7%
CO65
 
13.5%
N34
 
7.0%

Length

2021-05-25T18:22:58.354429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:22:58.545916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
se178
36.9%
s106
21.9%
ne100
20.7%
co65
 
13.5%
n34
 
7.0%

Most occurring characters

ValueCountFrequency (%)
S284
34.4%
E278
33.7%
N134
16.2%
C65
 
7.9%
O65
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter826
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S284
34.4%
E278
33.7%
N134
16.2%
C65
 
7.9%
O65
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
Latin826
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S284
34.4%
E278
33.7%
N134
16.2%
C65
 
7.9%
O65
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII826
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S284
34.4%
E278
33.7%
N134
16.2%
C65
 
7.9%
O65
 
7.9%

UF
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct27
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
SP
85 
MG
55 
PR
36 
SC
35 
RS
35 
Other values (22)
237 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters966
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.6%

Sample

1st rowGO
2nd rowGO
3rd rowCE
4th rowCE
5th rowGO

Common Values

ValueCountFrequency (%)
SP85
17.6%
MG55
11.4%
PR36
 
7.5%
SC35
 
7.2%
RS35
 
7.2%
PE30
 
6.2%
MS26
 
5.4%
RJ26
 
5.4%
CE23
 
4.8%
GO21
 
4.3%
Other values (17)111
23.0%

Length

2021-05-25T18:22:59.095448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp85
17.6%
mg55
11.4%
pr36
 
7.5%
sc35
 
7.2%
rs35
 
7.2%
pe30
 
6.2%
rj26
 
5.4%
ms26
 
5.4%
ce23
 
4.8%
go21
 
4.3%
Other values (17)111
23.0%

Most occurring characters

ValueCountFrequency (%)
S196
20.3%
P179
18.5%
R117
12.1%
M107
11.1%
G76
 
7.9%
E68
 
7.0%
C60
 
6.2%
A39
 
4.0%
O36
 
3.7%
J26
 
2.7%
Other values (7)62
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter966
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S196
20.3%
P179
18.5%
R117
12.1%
M107
11.1%
G76
 
7.9%
E68
 
7.0%
C60
 
6.2%
A39
 
4.0%
O36
 
3.7%
J26
 
2.7%
Other values (7)62
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Latin966
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S196
20.3%
P179
18.5%
R117
12.1%
M107
11.1%
G76
 
7.9%
E68
 
7.0%
C60
 
6.2%
A39
 
4.0%
O36
 
3.7%
J26
 
2.7%
Other values (7)62
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII966
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S196
20.3%
P179
18.5%
R117
12.1%
M107
11.1%
G76
 
7.9%
E68
 
7.0%
C60
 
6.2%
A39
 
4.0%
O36
 
3.7%
J26
 
2.7%
Other values (7)62
 
6.4%

ENTE
Categorical

HIGH CARDINALITY
UNIFORM

Distinct480
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
Rio Branco
 
2
Maracaju
 
2
Água Branca
 
2
Coxim
 
1
Águas Formosas
 
1
Other values (475)
475 

Length

Max length40
Median length9
Mean length11.10144928
Min length3

Characters and Unicode

Total characters5362
Distinct characters62
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique477 ?
Unique (%)98.8%

Sample

1st rowAbadia de Goiás
2nd rowAbadiânia
3rd rowAcarapé
4th rowAcopiara
5th rowAcreúna

Common Values

ValueCountFrequency (%)
Rio Branco2
 
0.4%
Maracaju2
 
0.4%
Água Branca2
 
0.4%
Coxim1
 
0.2%
Águas Formosas1
 
0.2%
Crato1
 
0.2%
Bom Sucesso1
 
0.2%
Marinópolis1
 
0.2%
Tapera1
 
0.2%
Taió1
 
0.2%
Other values (470)470
97.3%

Length

2021-05-25T18:22:59.842449image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
do80
 
9.5%
governo27
 
3.2%
estado26
 
3.1%
de24
 
2.9%
são11
 
1.3%
rio10
 
1.2%
sul10
 
1.2%
nova9
 
1.1%
da9
 
1.1%
serra7
 
0.8%
Other values (536)629
74.7%

Most occurring characters

ValueCountFrequency (%)
a805
15.0%
o485
 
9.0%
r422
 
7.9%
359
 
6.7%
i334
 
6.2%
e299
 
5.6%
d243
 
4.5%
n232
 
4.3%
s195
 
3.6%
t182
 
3.4%
Other values (52)1806
33.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4269
79.6%
Uppercase Letter725
 
13.5%
Space Separator359
 
6.7%
Other Punctuation5
 
0.1%
Dash Punctuation4
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a805
18.9%
o485
11.4%
r422
9.9%
i334
 
7.8%
e299
 
7.0%
d243
 
5.7%
n232
 
5.4%
s195
 
4.6%
t182
 
4.3%
u178
 
4.2%
Other values (26)894
20.9%
Uppercase Letter
ValueCountFrequency (%)
A100
13.8%
C74
 
10.2%
G64
 
8.8%
S59
 
8.1%
B46
 
6.3%
M43
 
5.9%
P40
 
5.5%
E38
 
5.2%
I37
 
5.1%
T35
 
4.8%
Other values (13)189
26.1%
Space Separator
ValueCountFrequency (%)
359
100.0%
Other Punctuation
ValueCountFrequency (%)
'5
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4994
93.1%
Common368
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a805
16.1%
o485
 
9.7%
r422
 
8.5%
i334
 
6.7%
e299
 
6.0%
d243
 
4.9%
n232
 
4.6%
s195
 
3.9%
t182
 
3.6%
u178
 
3.6%
Other values (49)1619
32.4%
Common
ValueCountFrequency (%)
359
97.6%
'5
 
1.4%
-4
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5155
96.1%
Latin 1 Sup207
 
3.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a805
15.6%
o485
 
9.4%
r422
 
8.2%
359
 
7.0%
i334
 
6.5%
e299
 
5.8%
d243
 
4.7%
n232
 
4.5%
s195
 
3.8%
t182
 
3.5%
Other values (40)1599
31.0%
Latin 1 Sup
ValueCountFrequency (%)
á38
18.4%
ã38
18.4%
í28
13.5%
é22
10.6%
ç21
10.1%
ó18
8.7%
Á12
 
5.8%
â10
 
4.8%
ú8
 
3.9%
ô6
 
2.9%
Other values (2)6
 
2.9%

ANO
Date

Distinct444
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
Minimum1895-08-21 00:00:00
Maximum2018-10-03 00:00:00
2021-05-25T18:23:00.251355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:23:00.620380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

N.JUR
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.8%
Missing10
Missing (%)2.1%
Memory size3.9 KiB
Autarquia
387 
Órgão Adm. Direta
54 
Fund. Dir. Púbico
 
22
Outros
 
10

Length

Max length17
Median length9
Mean length10.22198732
Min length6

Characters and Unicode

Total characters4835
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAutarquia
2nd rowAutarquia
3rd rowAutarquia
4th rowAutarquia
5th rowAutarquia

Common Values

ValueCountFrequency (%)
Autarquia387
80.1%
Órgão Adm. Direta54
 
11.2%
Fund. Dir. Púbico22
 
4.6%
Outros10
 
2.1%
(Missing)10
 
2.1%

Length

2021-05-25T18:23:01.402289image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:01.644640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
autarquia387
61.9%
órgão54
 
8.6%
adm54
 
8.6%
direta54
 
8.6%
púbico22
 
3.5%
dir22
 
3.5%
fund22
 
3.5%
outros10
 
1.6%

Most occurring characters

ValueCountFrequency (%)
a828
17.1%
u806
16.7%
r527
10.9%
i485
10.0%
t451
9.3%
A441
9.1%
q387
8.0%
152
 
3.1%
.98
 
2.0%
o86
 
1.8%
Other values (15)574
11.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3960
81.9%
Uppercase Letter625
 
12.9%
Space Separator152
 
3.1%
Other Punctuation98
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a828
20.9%
u806
20.4%
r527
13.3%
i485
12.2%
t451
11.4%
q387
9.8%
o86
 
2.2%
d76
 
1.9%
g54
 
1.4%
ã54
 
1.4%
Other values (7)206
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
A441
70.6%
D76
 
12.2%
Ó54
 
8.6%
F22
 
3.5%
P22
 
3.5%
O10
 
1.6%
Space Separator
ValueCountFrequency (%)
152
100.0%
Other Punctuation
ValueCountFrequency (%)
.98
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4585
94.8%
Common250
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a828
18.1%
u806
17.6%
r527
11.5%
i485
10.6%
t451
9.8%
A441
9.6%
q387
8.4%
o86
 
1.9%
d76
 
1.7%
D76
 
1.7%
Other values (13)422
9.2%
Common
ValueCountFrequency (%)
152
60.8%
.98
39.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII4705
97.3%
Latin 1 Sup130
 
2.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a828
17.6%
u806
17.1%
r527
11.2%
i485
10.3%
t451
9.6%
A441
9.4%
q387
8.2%
152
 
3.2%
.98
 
2.1%
o86
 
1.8%
Other values (12)444
9.4%
Latin 1 Sup
ValueCountFrequency (%)
Ó54
41.5%
ã54
41.5%
ú22
16.9%

POP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct453
Distinct (%)99.3%
Missing27
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean141151.5943
Minimum1160
Maximum2886698
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2021-05-25T18:23:01.937167image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1160
5-th percentile4695
Q116944.25
median40056
Q3125251.25
95-th percentile592024
Maximum2886698
Range2885538
Interquartile range (IQR)108307

Descriptive statistics

Standard deviation302852.9044
Coefficient of variation (CV)2.14558614
Kurtosis35.46774161
Mean141151.5943
Median Absolute Deviation (MAD)30405.5
Skewness5.277034408
Sum64365127
Variance9.171988168 × 1010
MonotonicityNot monotonic
2021-05-25T18:23:02.298200image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4134182
 
0.4%
480222
 
0.4%
174702
 
0.4%
184861
 
0.2%
436851
 
0.2%
5174511
 
0.2%
452201
 
0.2%
581621
 
0.2%
221761
 
0.2%
689901
 
0.2%
Other values (443)443
91.7%
(Missing)27
 
5.6%
ValueCountFrequency (%)
11601
0.2%
17991
0.2%
18181
0.2%
18651
0.2%
19831
0.2%
21061
0.2%
22511
0.2%
22841
0.2%
25091
0.2%
28401
0.2%
ValueCountFrequency (%)
28866981
0.2%
26866121
0.2%
22195801
0.2%
19486261
0.2%
16534611
0.2%
15360971
0.2%
14882521
0.2%
13921211
0.2%
9246241
0.2%
8680751
0.2%

TAM.ATV
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct457
Distinct (%)99.6%
Missing24
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean279573311.6
Minimum0
Maximum5911148873
Zeros2
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2021-05-25T18:23:02.666216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1732540.972
Q118593680.32
median55063371.15
Q3204031187.9
95-th percentile1315184810
Maximum5911148873
Range5911148873
Interquartile range (IQR)185437507.6

Descriptive statistics

Standard deviation725646343.2
Coefficient of variation (CV)2.595549407
Kurtosis29.57138033
Mean279573311.6
Median Absolute Deviation (MAD)46430308.32
Skewness5.095674096
Sum1.2832415 × 1011
Variance5.265626154 × 1017
MonotonicityNot monotonic
2021-05-25T18:23:03.030242image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02
 
0.4%
76516506.482
 
0.4%
162942121.51
 
0.2%
566299010.41
 
0.2%
54513189111
 
0.2%
124476494.21
 
0.2%
555648134.41
 
0.2%
199447470.71
 
0.2%
93575.91
 
0.2%
46775387.981
 
0.2%
Other values (447)447
92.5%
(Missing)24
 
5.0%
ValueCountFrequency (%)
02
0.4%
17269.161
0.2%
21391.071
0.2%
43696.211
0.2%
45994.771
0.2%
93575.91
0.2%
97280.141
0.2%
146201.691
0.2%
174719.811
0.2%
247827.871
0.2%
ValueCountFrequency (%)
59111488731
0.2%
57887259861
0.2%
54513189111
0.2%
47562718011
0.2%
46661870811
0.2%
40297881731
0.2%
37680349401
0.2%
33654820241
0.2%
29894114351
0.2%
28665062341
0.2%

AD.PROG
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
NÃO
282 
SIM
199 
SIM
 
2

Length

Max length4
Median length3
Mean length3.004140787
Min length3

Characters and Unicode

Total characters1451
Distinct characters7
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNÃO
2nd rowNÃO
3rd rowNÃO
4th rowNÃO
5th rowNÃO

Common Values

ValueCountFrequency (%)
NÃO282
58.4%
SIM199
41.2%
SIM 2
 
0.4%

Length

2021-05-25T18:23:03.740345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:03.949783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
não282
58.4%
sim201
41.6%

Most occurring characters

ValueCountFrequency (%)
N282
19.4%
Ã282
19.4%
O282
19.4%
S201
13.9%
I201
13.9%
M201
13.9%
2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1449
99.9%
Space Separator2
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N282
19.5%
Ã282
19.5%
O282
19.5%
S201
13.9%
I201
13.9%
M201
13.9%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1449
99.9%
Common2
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
N282
19.5%
Ã282
19.5%
O282
19.5%
S201
13.9%
I201
13.9%
M201
13.9%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1169
80.6%
Latin 1 Sup282
 
19.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N282
24.1%
O282
24.1%
S201
17.2%
I201
17.2%
M201
17.2%
2
 
0.2%
Latin 1 Sup
ValueCountFrequency (%)
Ã282
100.0%

C.PROG
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct7
Distinct (%)3.5%
Missing283
Missing (%)58.6%
Memory size3.9 KiB
NÃO
109 
NÍVEL I
49 
NÍVEL II
36 
NÍVEL IV
 
2
NÍVEL III
 
2
Other values (2)
 
2

Length

Max length9
Median length3
Mean length5.04
Min length3

Characters and Unicode

Total characters1008
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowNÃO
2nd rowNÃO
3rd rowNÃO
4th rowNÃO
5th rowNÃO

Common Values

ValueCountFrequency (%)
NÃO109
 
22.6%
NÍVEL I49
 
10.1%
NÍVEL II36
 
7.5%
NÍVEL IV2
 
0.4%
NÍVEL III2
 
0.4%
NIVEL II1
 
0.2%
NÍVEL I 1
 
0.2%
(Missing)283
58.6%

Length

2021-05-25T18:23:04.531228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:04.758621image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
não109
37.5%
nível90
30.9%
i50
17.2%
ii37
 
12.7%
iii2
 
0.7%
iv2
 
0.7%
nivel1
 
0.3%

Most occurring characters

ValueCountFrequency (%)
N200
19.8%
I133
13.2%
Ã109
10.8%
O109
10.8%
V93
9.2%
92
9.1%
E91
9.0%
L91
9.0%
Í90
8.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter916
90.9%
Space Separator92
 
9.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N200
21.8%
I133
14.5%
Ã109
11.9%
O109
11.9%
V93
10.2%
E91
9.9%
L91
9.9%
Í90
9.8%
Space Separator
ValueCountFrequency (%)
92
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin916
90.9%
Common92
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
N200
21.8%
I133
14.5%
Ã109
11.9%
O109
11.9%
V93
10.2%
E91
9.9%
L91
9.9%
Í90
9.8%
Common
ValueCountFrequency (%)
92
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII809
80.3%
Latin 1 Sup199
 
19.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N200
24.7%
I133
16.4%
O109
13.5%
V93
11.5%
92
11.4%
E91
11.2%
L91
11.2%
Latin 1 Sup
ValueCountFrequency (%)
Ã109
54.8%
Í90
45.2%

PRT_massa
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
MÉDIO PORTE
280 
PEQUENO PORTE
105 
GRANDE PORTE
70 
ESTADO/DF
 
27
NÃO CLASSIFICADO
 
1

Length

Max length16
Median length11
Mean length11.47826087
Min length9

Characters and Unicode

Total characters5544
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowPEQUENO PORTE
2nd rowPEQUENO PORTE
3rd rowPEQUENO PORTE
4th rowMÉDIO PORTE
5th rowMÉDIO PORTE

Common Values

ValueCountFrequency (%)
MÉDIO PORTE280
58.0%
PEQUENO PORTE105
 
21.7%
GRANDE PORTE70
 
14.5%
ESTADO/DF27
 
5.6%
NÃO CLASSIFICADO1
 
0.2%

Length

2021-05-25T18:23:05.394918image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:05.648245image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
porte455
48.5%
médio280
29.8%
pequeno105
 
11.2%
grande70
 
7.5%
estado/df27
 
2.9%
classificado1
 
0.1%
não1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
O869
15.7%
E762
13.7%
P560
10.1%
R525
9.5%
T482
8.7%
456
8.2%
D405
7.3%
I282
 
5.1%
M280
 
5.1%
É280
 
5.1%
Other values (11)643
11.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter5061
91.3%
Space Separator456
 
8.2%
Other Punctuation27
 
0.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O869
17.2%
E762
15.1%
P560
11.1%
R525
10.4%
T482
9.5%
D405
8.0%
I282
 
5.6%
M280
 
5.5%
É280
 
5.5%
N176
 
3.5%
Other values (9)440
8.7%
Space Separator
ValueCountFrequency (%)
456
100.0%
Other Punctuation
ValueCountFrequency (%)
/27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5061
91.3%
Common483
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
O869
17.2%
E762
15.1%
P560
11.1%
R525
10.4%
T482
9.5%
D405
8.0%
I282
 
5.6%
M280
 
5.5%
É280
 
5.5%
N176
 
3.5%
Other values (9)440
8.7%
Common
ValueCountFrequency (%)
456
94.4%
/27
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII5263
94.9%
Latin 1 Sup281
 
5.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O869
16.5%
E762
14.5%
P560
10.6%
R525
10.0%
T482
9.2%
456
8.7%
D405
7.7%
I282
 
5.4%
M280
 
5.3%
N176
 
3.3%
Other values (9)466
8.9%
Latin 1 Sup
ValueCountFrequency (%)
É280
99.6%
Ã1
 
0.4%

MassaSeg.
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
mais favorável
241 
menos favorável
214 
ESTADO/DF
27 
NÃO CLASSIFICADO
 
1

Length

Max length16
Median length14
Mean length14.16770186
Min length9

Characters and Unicode

Total characters6843
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowmais favorável
2nd rowmais favorável
3rd rowmais favorável
4th rowmais favorável
5th rowmenos favorável

Common Values

ValueCountFrequency (%)
mais favorável241
49.9%
menos favorável214
44.3%
ESTADO/DF27
 
5.6%
NÃO CLASSIFICADO1
 
0.2%

Length

2021-05-25T18:23:06.623632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:06.857211image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
favorável455
48.5%
mais241
25.7%
menos214
22.8%
estado/df27
 
2.9%
classificado1
 
0.1%
não1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
v910
13.3%
a696
10.2%
o669
9.8%
e669
9.8%
456
 
6.7%
m455
 
6.6%
s455
 
6.6%
f455
 
6.6%
r455
 
6.6%
á455
 
6.6%
Other values (16)1168
17.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6129
89.6%
Space Separator456
 
6.7%
Uppercase Letter231
 
3.4%
Other Punctuation27
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
v910
14.8%
a696
11.4%
o669
10.9%
e669
10.9%
m455
7.4%
s455
7.4%
f455
7.4%
r455
7.4%
á455
7.4%
l455
7.4%
Other values (2)455
7.4%
Uppercase Letter
ValueCountFrequency (%)
D55
23.8%
S29
12.6%
A29
12.6%
O29
12.6%
F28
12.1%
E27
11.7%
T27
11.7%
C2
 
0.9%
I2
 
0.9%
N1
 
0.4%
Other values (2)2
 
0.9%
Space Separator
ValueCountFrequency (%)
456
100.0%
Other Punctuation
ValueCountFrequency (%)
/27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6360
92.9%
Common483
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
v910
14.3%
a696
10.9%
o669
10.5%
e669
10.5%
m455
7.2%
s455
7.2%
f455
7.2%
r455
7.2%
á455
7.2%
l455
7.2%
Other values (14)686
10.8%
Common
ValueCountFrequency (%)
456
94.4%
/27
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII6387
93.3%
Latin 1 Sup456
 
6.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
v910
14.2%
a696
10.9%
o669
10.5%
e669
10.5%
456
7.1%
m455
7.1%
s455
7.1%
f455
7.1%
r455
7.1%
l455
7.1%
Other values (14)712
11.1%
Latin 1 Sup
ValueCountFrequency (%)
á455
99.8%
Ã1
 
0.2%

GR_POD
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
1
483 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters483
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1483
100.0%

Length

2021-05-25T18:23:07.393775image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:07.555345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1483
100.0%

Most occurring characters

ValueCountFrequency (%)
1483
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number483
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1483
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common483
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1483
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII483
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1483
100.0%

GR_SEGR
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing1
Missing (%)0.2%
Memory size3.9 KiB
1.0
399 
0.0
83 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1446
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0399
82.6%
0.083
 
17.2%
(Missing)1
 
0.2%

Length

2021-05-25T18:23:07.990180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:08.151750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0399
82.8%
0.083
 
17.2%

Most occurring characters

ValueCountFrequency (%)
0565
39.1%
.482
33.3%
1399
27.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number964
66.7%
Other Punctuation482
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0565
58.6%
1399
41.4%
Other Punctuation
ValueCountFrequency (%)
.482
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1446
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0565
39.1%
.482
33.3%
1399
27.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0565
39.1%
.482
33.3%
1399
27.6%

GR_C.ÉT
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing3
Missing (%)0.6%
Memory size3.9 KiB
0.0
337 
1.0
143 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1440
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0337
69.8%
1.0143
29.6%
(Missing)3
 
0.6%

Length

2021-05-25T18:23:08.643434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:08.827195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0337
70.2%
1.0143
29.8%

Most occurring characters

ValueCountFrequency (%)
0817
56.7%
.480
33.3%
1143
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number960
66.7%
Other Punctuation480
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0817
85.1%
1143
 
14.9%
Other Punctuation
ValueCountFrequency (%)
.480
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0817
56.7%
.480
33.3%
1143
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0817
56.7%
.480
33.3%
1143
 
9.9%

CIE_REL
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
0
316 
1
167 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters483
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0316
65.4%
1167
34.6%

Length

2021-05-25T18:23:09.317885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:09.486432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0316
65.4%
1167
34.6%

Most occurring characters

ValueCountFrequency (%)
0316
65.4%
1167
34.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number483
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0316
65.4%
1167
34.6%

Most occurring scripts

ValueCountFrequency (%)
Common483
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0316
65.4%
1167
34.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII483
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0316
65.4%
1167
34.6%

CIE_FISC
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.6%
Missing4
Missing (%)0.8%
Memory size3.9 KiB
REG
445 
JUD
 
22
IRG
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1437
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowREG
2nd rowREG
3rd rowREG
4th rowREG
5th rowREG

Common Values

ValueCountFrequency (%)
REG445
92.1%
JUD22
 
4.6%
IRG12
 
2.5%
(Missing)4
 
0.8%

Length

2021-05-25T18:23:09.941467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:10.116997image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
reg445
92.9%
jud22
 
4.6%
irg12
 
2.5%

Most occurring characters

ValueCountFrequency (%)
R457
31.8%
G457
31.8%
E445
31.0%
J22
 
1.5%
U22
 
1.5%
D22
 
1.5%
I12
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1437
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R457
31.8%
G457
31.8%
E445
31.0%
J22
 
1.5%
U22
 
1.5%
D22
 
1.5%
I12
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin1437
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R457
31.8%
G457
31.8%
E445
31.0%
J22
 
1.5%
U22
 
1.5%
D22
 
1.5%
I12
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1437
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R457
31.8%
G457
31.8%
E445
31.0%
J22
 
1.5%
U22
 
1.5%
D22
 
1.5%
I12
 
0.8%

CIE_AUD
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
REG
474 
JUD
 
5
IRG
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1449
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowREG
2nd rowREG
3rd rowREG
4th rowREG
5th rowREG

Common Values

ValueCountFrequency (%)
REG474
98.1%
JUD5
 
1.0%
IRG4
 
0.8%

Length

2021-05-25T18:23:10.476040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:10.609680image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
reg474
98.1%
jud5
 
1.0%
irg4
 
0.8%

Most occurring characters

ValueCountFrequency (%)
R478
33.0%
G478
33.0%
E474
32.7%
J5
 
0.3%
U5
 
0.3%
D5
 
0.3%
I4
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1449
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R478
33.0%
G478
33.0%
E474
32.7%
J5
 
0.3%
U5
 
0.3%
D5
 
0.3%
I4
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin1449
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R478
33.0%
G478
33.0%
E474
32.7%
J5
 
0.3%
U5
 
0.3%
D5
 
0.3%
I4
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1449
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R478
33.0%
G478
33.0%
E474
32.7%
J5
 
0.3%
U5
 
0.3%
D5
 
0.3%
I4
 
0.3%

CIE_CRP
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.6%
Missing1
Missing (%)0.2%
Memory size3.9 KiB
REG
251 
JUD
149 
IRG
82 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1446
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIRG
2nd rowJUD
3rd rowIRG
4th rowREG
5th rowREG

Common Values

ValueCountFrequency (%)
REG251
52.0%
JUD149
30.8%
IRG82
 
17.0%
(Missing)1
 
0.2%

Length

2021-05-25T18:23:10.983681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:11.088398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
reg251
52.1%
jud149
30.9%
irg82
 
17.0%

Most occurring characters

ValueCountFrequency (%)
R333
23.0%
G333
23.0%
E251
17.4%
J149
10.3%
U149
10.3%
D149
10.3%
I82
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R333
23.0%
G333
23.0%
E251
17.4%
J149
10.3%
U149
10.3%
D149
10.3%
I82
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Latin1446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R333
23.0%
G333
23.0%
E251
17.4%
J149
10.3%
U149
10.3%
D149
10.3%
I82
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R333
23.0%
G333
23.0%
E251
17.4%
J149
10.3%
U149
10.3%
D149
10.3%
I82
 
5.7%

GI_DIV.CART
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.6%
Missing1
Missing (%)0.2%
Memory size3.9 KiB
REG
447 
JUD
 
22
IRG
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1446
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowREG
2nd rowREG
3rd rowREG
4th rowREG
5th rowREG

Common Values

ValueCountFrequency (%)
REG447
92.5%
JUD22
 
4.6%
IRG13
 
2.7%
(Missing)1
 
0.2%

Length

2021-05-25T18:23:11.434509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:11.561169image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
reg447
92.7%
jud22
 
4.6%
irg13
 
2.7%

Most occurring characters

ValueCountFrequency (%)
R460
31.8%
G460
31.8%
E447
30.9%
J22
 
1.5%
U22
 
1.5%
D22
 
1.5%
I13
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1446
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R460
31.8%
G460
31.8%
E447
30.9%
J22
 
1.5%
U22
 
1.5%
D22
 
1.5%
I13
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin1446
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R460
31.8%
G460
31.8%
E447
30.9%
J22
 
1.5%
U22
 
1.5%
D22
 
1.5%
I13
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R460
31.8%
G460
31.8%
E447
30.9%
J22
 
1.5%
U22
 
1.5%
D22
 
1.5%
I13
 
0.9%

GI_C.TÉC
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.6%
Missing3
Missing (%)0.6%
Memory size3.9 KiB
B
332 
C
90 
A
58 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters480
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowC
3rd rowC
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
B332
68.7%
C90
 
18.6%
A58
 
12.0%
(Missing)3
 
0.6%

Length

2021-05-25T18:23:11.881313image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:12.054849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
b332
69.2%
c90
 
18.8%
a58
 
12.1%

Most occurring characters

ValueCountFrequency (%)
B332
69.2%
C90
 
18.8%
A58
 
12.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter480
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B332
69.2%
C90
 
18.8%
A58
 
12.1%

Most occurring scripts

ValueCountFrequency (%)
Latin480
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B332
69.2%
C90
 
18.8%
A58
 
12.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B332
69.2%
C90
 
18.8%
A58
 
12.1%

GI_P.INV
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
1
446 
0
 
37

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters483
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1446
92.3%
037
 
7.7%

Length

2021-05-25T18:23:12.507638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:12.627316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1446
92.3%
037
 
7.7%

Most occurring characters

ValueCountFrequency (%)
1446
92.3%
037
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number483
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1446
92.3%
037
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Common483
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1446
92.3%
037
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII483
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1446
92.3%
037
 
7.7%

GI_COMITÊ
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
1
412 
0
71 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters483
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1412
85.3%
071
 
14.7%

Length

2021-05-25T18:23:13.045200image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:13.201782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1412
85.3%
071
 
14.7%

Most occurring characters

ValueCountFrequency (%)
1412
85.3%
071
 
14.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number483
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1412
85.3%
071
 
14.7%

Most occurring scripts

ValueCountFrequency (%)
Common483
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1412
85.3%
071
 
14.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII483
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1412
85.3%
071
 
14.7%

GI_CONS
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.4%
Missing12
Missing (%)2.5%
Memory size3.9 KiB
1.0
297 
0.0
174 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1413
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0297
61.5%
0.0174
36.0%
(Missing)12
 
2.5%

Length

2021-05-25T18:23:13.715831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:13.890881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0297
63.1%
0.0174
36.9%

Most occurring characters

ValueCountFrequency (%)
0645
45.6%
.471
33.3%
1297
21.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number942
66.7%
Other Punctuation471
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0645
68.5%
1297
31.5%
Other Punctuation
ValueCountFrequency (%)
.471
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0645
45.6%
.471
33.3%
1297
21.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0645
45.6%
.471
33.3%
1297
21.0%

SA_AMRTZ
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
0
327 
1
156 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters483
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0327
67.7%
1156
32.3%

Length

2021-05-25T18:23:14.345665image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:14.512218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0327
67.7%
1156
32.3%

Most occurring characters

ValueCountFrequency (%)
0327
67.7%
1156
32.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number483
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0327
67.7%
1156
32.3%

Most occurring scripts

ValueCountFrequency (%)
Common483
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0327
67.7%
1156
32.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII483
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0327
67.7%
1156
32.3%

SA_PUB.REL
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
REG
316 
JUD
95 
IRG
72 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1449
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIRG
2nd rowJUD
3rd rowREG
4th rowREG
5th rowREG

Common Values

ValueCountFrequency (%)
REG316
65.4%
JUD95
 
19.7%
IRG72
 
14.9%

Length

2021-05-25T18:23:15.017831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:15.210315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
reg316
65.4%
jud95
 
19.7%
irg72
 
14.9%

Most occurring characters

ValueCountFrequency (%)
R388
26.8%
G388
26.8%
E316
21.8%
J95
 
6.6%
U95
 
6.6%
D95
 
6.6%
I72
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1449
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R388
26.8%
G388
26.8%
E316
21.8%
J95
 
6.6%
U95
 
6.6%
D95
 
6.6%
I72
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1449
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R388
26.8%
G388
26.8%
E316
21.8%
J95
 
6.6%
U95
 
6.6%
D95
 
6.6%
I72
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1449
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R388
26.8%
G388
26.8%
E316
21.8%
J95
 
6.6%
U95
 
6.6%
D95
 
6.6%
I72
 
5.0%

SA_REPAS
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.6%
Missing5
Missing (%)1.0%
Memory size3.9 KiB
REG
391 
JUD
57 
IRG
 
30

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1434
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowREG
2nd rowREG
3rd rowIRG
4th rowREG
5th rowREG

Common Values

ValueCountFrequency (%)
REG391
81.0%
JUD57
 
11.8%
IRG30
 
6.2%
(Missing)5
 
1.0%

Length

2021-05-25T18:23:15.760844image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:15.935376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
reg391
81.8%
jud57
 
11.9%
irg30
 
6.3%

Most occurring characters

ValueCountFrequency (%)
R421
29.4%
G421
29.4%
E391
27.3%
J57
 
4.0%
U57
 
4.0%
D57
 
4.0%
I30
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1434
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R421
29.4%
G421
29.4%
E391
27.3%
J57
 
4.0%
U57
 
4.0%
D57
 
4.0%
I30
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin1434
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R421
29.4%
G421
29.4%
E391
27.3%
J57
 
4.0%
U57
 
4.0%
D57
 
4.0%
I30
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1434
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R421
29.4%
G421
29.4%
E391
27.3%
J57
 
4.0%
U57
 
4.0%
D57
 
4.0%
I30
 
2.1%

SA_R.ALIQ
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing1
Missing (%)0.2%
Memory size3.9 KiB
0.0
252 
1.0
230 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1446
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0252
52.2%
1.0230
47.6%
(Missing)1
 
0.2%

Length

2021-05-25T18:23:16.402887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:16.642246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0252
52.3%
1.0230
47.7%

Most occurring characters

ValueCountFrequency (%)
0734
50.8%
.482
33.3%
1230
 
15.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number964
66.7%
Other Punctuation482
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0734
76.1%
1230
 
23.9%
Other Punctuation
ValueCountFrequency (%)
.482
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1446
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0734
50.8%
.482
33.3%
1230
 
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0734
50.8%
.482
33.3%
1230
 
15.9%

GTI_POL.INFR.
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
0
368 
1
115 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters483
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0368
76.2%
1115
 
23.8%

Length

2021-05-25T18:23:17.080077image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:17.261081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0368
76.2%
1115
 
23.8%

Most occurring characters

ValueCountFrequency (%)
0368
76.2%
1115
 
23.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number483
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0368
76.2%
1115
 
23.8%

Most occurring scripts

ValueCountFrequency (%)
Common483
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0368
76.2%
1115
 
23.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII483
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0368
76.2%
1115
 
23.8%

GTI_INV.TI
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing1
Missing (%)0.2%
Memory size3.9 KiB
1.0
248 
0.0
234 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1446
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0248
51.3%
0.0234
48.4%
(Missing)1
 
0.2%

Length

2021-05-25T18:23:17.737807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:17.922316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0248
51.5%
0.0234
48.5%

Most occurring characters

ValueCountFrequency (%)
0716
49.5%
.482
33.3%
1248
 
17.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number964
66.7%
Other Punctuation482
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0716
74.3%
1248
 
25.7%
Other Punctuation
ValueCountFrequency (%)
.482
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1446
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0716
49.5%
.482
33.3%
1248
 
17.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0716
49.5%
.482
33.3%
1248
 
17.2%

DDM_CART
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing3
Missing (%)0.6%
Memory size3.9 KiB
1.0
425 
0.0
55 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1440
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0425
88.0%
0.055
 
11.4%
(Missing)3
 
0.6%

Length

2021-05-25T18:23:18.385076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:18.565593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0425
88.5%
0.055
 
11.5%

Most occurring characters

ValueCountFrequency (%)
0535
37.2%
.480
33.3%
1425
29.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number960
66.7%
Other Punctuation480
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0535
55.7%
1425
44.3%
Other Punctuation
ValueCountFrequency (%)
.480
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0535
37.2%
.480
33.3%
1425
29.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0535
37.2%
.480
33.3%
1425
29.5%

DDM_ACES
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing2
Missing (%)0.4%
Memory size3.9 KiB
REG
480 
JUD
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1443
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowREG
2nd rowREG
3rd rowREG
4th rowREG
5th rowREG

Common Values

ValueCountFrequency (%)
REG480
99.4%
JUD1
 
0.2%
(Missing)2
 
0.4%

Length

2021-05-25T18:23:19.007411image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:19.181945image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
reg480
99.8%
jud1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
R480
33.3%
E480
33.3%
G480
33.3%
J1
 
0.1%
U1
 
0.1%
D1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1443
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R480
33.3%
E480
33.3%
G480
33.3%
J1
 
0.1%
U1
 
0.1%
D1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin1443
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R480
33.3%
E480
33.3%
G480
33.3%
J1
 
0.1%
U1
 
0.1%
D1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1443
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R480
33.3%
E480
33.3%
G480
33.3%
J1
 
0.1%
U1
 
0.1%
D1
 
0.1%

DDM_ATAS
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.4%
Missing5
Missing (%)1.0%
Memory size3.9 KiB
1.0
324 
0.0
154 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1434
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0324
67.1%
0.0154
31.9%
(Missing)5
 
1.0%

Length

2021-05-25T18:23:19.743053image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:19.959475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0324
67.8%
0.0154
32.2%

Most occurring characters

ValueCountFrequency (%)
0632
44.1%
.478
33.3%
1324
22.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number956
66.7%
Other Punctuation478
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0632
66.1%
1324
33.9%
Other Punctuation
ValueCountFrequency (%)
.478
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1434
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0632
44.1%
.478
33.3%
1324
22.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1434
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0632
44.1%
.478
33.3%
1324
22.6%

RP_REG.ENV
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.6%
Missing3
Missing (%)0.6%
Memory size3.9 KiB
A
202 
B
181 
C
97 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters480
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowB
3rd rowB
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A202
41.8%
B181
37.5%
C97
20.1%
(Missing)3
 
0.6%

Length

2021-05-25T18:23:20.948096image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:21.123627image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
a202
42.1%
b181
37.7%
c97
20.2%

Most occurring characters

ValueCountFrequency (%)
A202
42.1%
B181
37.7%
C97
20.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter480
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A202
42.1%
B181
37.7%
C97
20.2%

Most occurring scripts

ValueCountFrequency (%)
Latin480
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A202
42.1%
B181
37.7%
C97
20.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A202
42.1%
B181
37.7%
C97
20.2%

RP_REL.GOV
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.4%
Missing5
Missing (%)1.0%
Memory size3.9 KiB
0.0
331 
1.0
147 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1434
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0331
68.5%
1.0147
30.4%
(Missing)5
 
1.0%

Length

2021-05-25T18:23:21.602347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:21.776880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0331
69.2%
1.0147
30.8%

Most occurring characters

ValueCountFrequency (%)
0809
56.4%
.478
33.3%
1147
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number956
66.7%
Other Punctuation478
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0809
84.6%
1147
 
15.4%
Other Punctuation
ValueCountFrequency (%)
.478
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1434
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0809
56.4%
.478
33.3%
1147
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1434
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0809
56.4%
.478
33.3%
1147
 
10.3%

RP_SITE
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing1
Missing (%)0.2%
Memory size3.9 KiB
1.0
419 
0.0
63 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1446
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0419
86.7%
0.063
 
13.0%
(Missing)1
 
0.2%

Length

2021-05-25T18:23:22.265575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:22.442101image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0419
86.9%
0.063
 
13.1%

Most occurring characters

ValueCountFrequency (%)
0545
37.7%
.482
33.3%
1419
29.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number964
66.7%
Other Punctuation482
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0545
56.5%
1419
43.5%
Other Punctuation
ValueCountFrequency (%)
.482
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1446
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0545
37.7%
.482
33.3%
1419
29.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0545
37.7%
.482
33.3%
1419
29.0%

PS_OUV
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing4
Missing (%)0.8%
Memory size3.9 KiB
1.0
403 
0.0
76 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1437
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0403
83.4%
0.076
 
15.7%
(Missing)4
 
0.8%

Length

2021-05-25T18:23:22.913944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:23.112413image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0403
84.1%
0.076
 
15.9%

Most occurring characters

ValueCountFrequency (%)
0555
38.6%
.479
33.3%
1403
28.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number958
66.7%
Other Punctuation479
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0555
57.9%
1403
42.1%
Other Punctuation
ValueCountFrequency (%)
.479
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1437
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0555
38.6%
.479
33.3%
1403
28.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1437
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0555
38.6%
.479
33.3%
1403
28.0%

PS_GEST
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing1
Missing (%)0.2%
Memory size3.9 KiB
0.0
376 
1.0
106 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1446
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0376
77.8%
1.0106
 
21.9%
(Missing)1
 
0.2%

Length

2021-05-25T18:23:23.633282image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:23.793854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0376
78.0%
1.0106
 
22.0%

Most occurring characters

ValueCountFrequency (%)
0858
59.3%
.482
33.3%
1106
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number964
66.7%
Other Punctuation482
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0858
89.0%
1106
 
11.0%
Other Punctuation
ValueCountFrequency (%)
.482
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1446
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0858
59.3%
.482
33.3%
1106
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0858
59.3%
.482
33.3%
1106
 
7.3%

IP_REP.CF
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.6%
Missing16
Missing (%)3.3%
Memory size3.9 KiB
A
185 
B
178 
C
104 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters467
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowC
3rd rowB
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A185
38.3%
B178
36.9%
C104
21.5%
(Missing)16
 
3.3%

Length

2021-05-25T18:23:24.293519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:24.462066image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
a185
39.6%
b178
38.1%
c104
22.3%

Most occurring characters

ValueCountFrequency (%)
A185
39.6%
B178
38.1%
C104
22.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter467
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A185
39.6%
B178
38.1%
C104
22.3%

Most occurring scripts

ValueCountFrequency (%)
Latin467
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A185
39.6%
B178
38.1%
C104
22.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII467
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A185
39.6%
B178
38.1%
C104
22.3%

IP_SELEC
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing2
Missing (%)0.4%
Memory size3.9 KiB
ESC
381 
EL
100 

Length

Max length3
Median length3
Mean length2.792099792
Min length2

Characters and Unicode

Total characters1343
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEL
2nd rowEL
3rd rowESC
4th rowESC
5th rowEL

Common Values

ValueCountFrequency (%)
ESC381
78.9%
EL100
 
20.7%
(Missing)2
 
0.4%

Length

2021-05-25T18:23:24.951756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:25.139255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
esc381
79.2%
el100
 
20.8%

Most occurring characters

ValueCountFrequency (%)
E481
35.8%
S381
28.4%
C381
28.4%
L100
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1343
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E481
35.8%
S381
28.4%
C381
28.4%
L100
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Latin1343
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E481
35.8%
S381
28.4%
C381
28.4%
L100
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E481
35.8%
S381
28.4%
C381
28.4%
L100
 
7.4%

LI_CERT.MB
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.6%
Missing16
Missing (%)3.3%
Memory size3.9 KiB
C
326 
B
123 
A
 
18

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters467
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
C326
67.5%
B123
 
25.5%
A18
 
3.7%
(Missing)16
 
3.3%

Length

2021-05-25T18:23:25.625478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:25.800714image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
c326
69.8%
b123
 
26.3%
a18
 
3.9%

Most occurring characters

ValueCountFrequency (%)
C326
69.8%
B123
 
26.3%
A18
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter467
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C326
69.8%
B123
 
26.3%
A18
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Latin467
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C326
69.8%
B123
 
26.3%
A18
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII467
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C326
69.8%
B123
 
26.3%
A18
 
3.9%

LI_DIR.CERT
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
1
375 
0
108 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters483
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1375
77.6%
0108
 
22.4%

Length

2021-05-25T18:23:26.268463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:26.436016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1375
77.6%
0108
 
22.4%

Most occurring characters

ValueCountFrequency (%)
1375
77.6%
0108
 
22.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number483
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1375
77.6%
0108
 
22.4%

Most occurring scripts

ValueCountFrequency (%)
Common483
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1375
77.6%
0108
 
22.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII483
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1375
77.6%
0108
 
22.4%

LI_P.EST.
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing1
Missing (%)0.2%
Memory size3.9 KiB
0.0
340 
1.0
142 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1446
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0340
70.4%
1.0142
29.4%
(Missing)1
 
0.2%

Length

2021-05-25T18:23:26.918202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T18:23:27.086752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0340
70.5%
1.0142
29.5%

Most occurring characters

ValueCountFrequency (%)
0822
56.8%
.482
33.3%
1142
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number964
66.7%
Other Punctuation482
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0822
85.3%
1142
 
14.7%
Other Punctuation
ValueCountFrequency (%)
.482
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1446
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0822
56.8%
.482
33.3%
1142
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0822
56.8%
.482
33.3%
1142
 
9.8%

R.FIN
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct450
Distinct (%)99.3%
Missing30
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean-163181126.2
Minimum-1.701491796 × 1010
Maximum875482695.9
Zeros0
Zeros (%)0.0%
Negative175
Negative (%)36.2%
Memory size3.9 KiB
2021-05-25T18:23:27.313144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1.701491796 × 1010
5-th percentile-323537271.8
Q1-2666727.3
median1544334.33
Q311258936.5
95-th percentile63448488.4
Maximum875482695.9
Range1.789040066 × 1010
Interquartile range (IQR)13925663.8

Descriptive statistics

Standard deviation1227011676
Coefficient of variation (CV)-7.519323496
Kurtosis115.208654
Mean-163181126.2
Median Absolute Deviation (MAD)6061261.38
Skewness-10.13106641
Sum-7.392105017 × 1010
Variance1.505557654 × 1018
MonotonicityNot monotonic
2021-05-25T18:23:27.687143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-354697.622
 
0.4%
91572003.132
 
0.4%
2829015.752
 
0.4%
-13828676.051
 
0.2%
630624480.81
 
0.2%
62314.41
 
0.2%
-4937919.221
 
0.2%
1319148.911
 
0.2%
-2383647.091
 
0.2%
1106238411
 
0.2%
Other values (440)440
91.1%
(Missing)30
 
6.2%
ValueCountFrequency (%)
-1.701491796 × 10101
0.2%
-1.255354433 × 10101
0.2%
-1.041227888 × 10101
0.2%
-78858848801
0.2%
-47413009861
0.2%
-33329814461
0.2%
-31295378151
0.2%
-29861352211
0.2%
-24295009581
0.2%
-20177760731
0.2%
ValueCountFrequency (%)
875482695.91
0.2%
630624480.81
0.2%
452718416.61
0.2%
248984384.61
0.2%
207698621.21
0.2%
1962114531
0.2%
125347268.81
0.2%
122063433.71
0.2%
1106238411
0.2%
99548187.131
0.2%

IND.SF
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct446
Distinct (%)98.2%
Missing29
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean1.80716749
Minimum-1.093554999 × 10-16
Maximum29.70066657
Zeros6
Zeros (%)1.2%
Negative1
Negative (%)0.2%
Memory size3.9 KiB
2021-05-25T18:23:28.078013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1.093554999 × 10-16
5-th percentile0.4012543475
Q10.8014003234
median1.247655843
Q32.191206375
95-th percentile4.459868052
Maximum29.70066657
Range29.70066657
Interquartile range (IQR)1.389806051

Descriptive statistics

Standard deviation2.016202241
Coefficient of variation (CV)1.115669827
Kurtosis83.49025254
Mean1.80716749
Median Absolute Deviation (MAD)0.5728141392
Skewness7.006730694
Sum820.4540404
Variance4.065071475
MonotonicityNot monotonic
2021-05-25T18:23:28.441556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06
 
1.2%
1.2148393432
 
0.4%
3.2289195722
 
0.4%
0.87201054572
 
0.4%
1.3001274991
 
0.2%
2.7660441521
 
0.2%
1.5937395341
 
0.2%
0.77241986391
 
0.2%
1.0118234061
 
0.2%
1.6648401151
 
0.2%
Other values (436)436
90.3%
(Missing)29
 
6.0%
ValueCountFrequency (%)
-1.093554999 × 10-161
 
0.2%
06
1.2%
0.10896662891
 
0.2%
0.15955886261
 
0.2%
0.16209608331
 
0.2%
0.20361743221
 
0.2%
0.2427737491
 
0.2%
0.24765867281
 
0.2%
0.25658221211
 
0.2%
0.29497548141
 
0.2%
ValueCountFrequency (%)
29.700666571
0.2%
11.455029491
0.2%
10.932463831
0.2%
10.819133271
0.2%
9.7638923281
0.2%
8.9226690321
0.2%
7.2644014041
0.2%
6.7185684491
0.2%
6.6084585611
0.2%
6.5402220561
0.2%

R.INV
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct457
Distinct (%)99.3%
Missing23
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean37604066.64
Minimum-2151664669
Maximum1220762395
Zeros1
Zeros (%)0.2%
Negative52
Negative (%)10.8%
Memory size3.9 KiB
2021-05-25T18:23:28.836499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2151664669
5-th percentile-2135781.922
Q12230268.38
median7858158.695
Q331813110.21
95-th percentile178624675.9
Maximum1220762395
Range3372427063
Interquartile range (IQR)29582841.83

Descriptive statistics

Standard deviation158581892
Coefficient of variation (CV)4.217147402
Kurtosis91.05744841
Mean37604066.64
Median Absolute Deviation (MAD)7804801.745
Skewness-3.400565898
Sum1.729787066 × 1010
Variance2.514821646 × 1016
MonotonicityNot monotonic
2021-05-25T18:23:29.223393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90215306.372
 
0.4%
-339686.382
 
0.4%
9808592.092
 
0.4%
5066195.281
 
0.2%
6655564.671
 
0.2%
11206110.61
 
0.2%
-2801179.481
 
0.2%
-2236857.721
 
0.2%
60727201.91
 
0.2%
9491902231
 
0.2%
Other values (447)447
92.5%
(Missing)23
 
4.8%
ValueCountFrequency (%)
-21516646691
0.2%
-431129345.91
0.2%
-2656166221
0.2%
-48203871.341
0.2%
-42856336.281
0.2%
-26744651.091
0.2%
-22574067.491
0.2%
-14659401.151
0.2%
-11735045.931
0.2%
-11095125.221
0.2%
ValueCountFrequency (%)
12207623951
0.2%
9491902231
0.2%
845141645.11
0.2%
824575279.51
0.2%
623691267.11
0.2%
618817308.91
0.2%
551628734.71
0.2%
4907639351
0.2%
464028616.71
0.2%
424901190.81
0.2%

VAR.INV
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct456
Distinct (%)99.3%
Missing24
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean0.3174280104
Minimum-0.99701821
Maximum13.62771481
Zeros0
Zeros (%)0.0%
Negative52
Negative (%)10.8%
Memory size3.9 KiB
2021-05-25T18:23:29.594914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.99701821
5-th percentile-0.1757282473
Q10.1169265351
median0.1844074437
Q30.2454681355
95-th percentile0.6262406429
Maximum13.62771481
Range14.62473302
Interquartile range (IQR)0.1285416004

Descriptive statistics

Standard deviation1.14964428
Coefficient of variation (CV)3.621748056
Kurtosis78.55715147
Mean0.3174280104
Median Absolute Deviation (MAD)0.06535249045
Skewness8.338313836
Sum145.6994568
Variance1.321681969
MonotonicityNot monotonic
2021-05-25T18:23:29.941502image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.23089103252
 
0.4%
-0.062808847322
 
0.4%
0.14703790672
 
0.4%
0.12325896331
 
0.2%
0.18611837631
 
0.2%
0.19799285441
 
0.2%
0.017267190091
 
0.2%
0.13164433661
 
0.2%
0.72293338711
 
0.2%
0.22517059641
 
0.2%
Other values (446)446
92.3%
(Missing)24
 
5.0%
ValueCountFrequency (%)
-0.997018211
0.2%
-0.94968650311
0.2%
-0.78063167181
0.2%
-0.7805078331
0.2%
-0.72566823551
0.2%
-0.64585494941
0.2%
-0.60952710571
0.2%
-0.59241527021
0.2%
-0.57863070451
0.2%
-0.56498033511
0.2%
ValueCountFrequency (%)
13.627714811
0.2%
11.89410881
0.2%
10.032967681
0.2%
7.5052953871
0.2%
6.8296310841
0.2%
6.4038649921
0.2%
4.1069762211
0.2%
3.1131275111
0.2%
2.3751384461
0.2%
1.7207281111
0.2%

R.AT
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct411
Distinct (%)99.5%
Missing70
Missing (%)14.5%
Infinite0
Infinite (%)0.0%
Mean-4921375352
Minimum-3.470908443 × 1011
Maximum38495499.24
Zeros0
Zeros (%)0.0%
Negative406
Negative (%)84.1%
Memory size3.9 KiB
2021-05-25T18:23:30.239226image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.470908443 × 1011
5-th percentile-1.274789586 × 1010
Q1-586788614.3
median-139066840.8
Q3-48998953.69
95-th percentile-11444562.98
Maximum38495499.24
Range3.471293398 × 1011
Interquartile range (IQR)537789660.6

Descriptive statistics

Standard deviation2.804579137 × 1010
Coefficient of variation (CV)-5.698771048
Kurtosis91.6647327
Mean-4921375352
Median Absolute Deviation (MAD)111029049
Skewness-9.071377212
Sum-2.03252802 × 1012
Variance7.865664135 × 1020
MonotonicityNot monotonic
2021-05-25T18:23:30.486658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-295511611.22
 
0.4%
-36341130.142
 
0.4%
-7.683254905 × 10101
 
0.2%
-993867365.91
 
0.2%
-16076887.451
 
0.2%
-1.312349732 × 10101
 
0.2%
-16745308661
 
0.2%
-72459138.781
 
0.2%
-46424138.91
 
0.2%
-10369522991
 
0.2%
Other values (401)401
83.0%
(Missing)70
 
14.5%
ValueCountFrequency (%)
-3.470908443 × 10111
0.2%
-2.749190753 × 10111
0.2%
-2.570615879 × 10111
0.2%
-1.462154403 × 10111
0.2%
-1.198480442 × 10111
0.2%
-1.010404685 × 10111
0.2%
-7.683254905 × 10101
0.2%
-6.725115857 × 10101
0.2%
-6.491523952 × 10101
0.2%
-5.190426728 × 10101
0.2%
ValueCountFrequency (%)
38495499.241
0.2%
31638205.621
0.2%
4109841.571
0.2%
3322750.381
0.2%
2080753.7651
0.2%
1526431.591
0.2%
632264.941
0.2%
-2967468.451
0.2%
-5208626.631
0.2%
-6199158.9281
0.2%

IND.COB
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct410
Distinct (%)99.3%
Missing70
Missing (%)14.5%
Infinite0
Infinite (%)0.0%
Mean0.3296002051
Minimum-0.08869110907
Maximum1.079495081
Zeros2
Zeros (%)0.4%
Negative1
Negative (%)0.2%
Memory size3.9 KiB
2021-05-25T18:23:30.756938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.08869110907
5-th percentile0.003709156719
Q10.1298469255
median0.3033047549
Q30.4886125866
95-th percentile0.7808489926
Maximum1.079495081
Range1.168186191
Interquartile range (IQR)0.3587656611

Descriptive statistics

Standard deviation0.2461518329
Coefficient of variation (CV)0.7468194166
Kurtosis-0.1563872622
Mean0.3296002051
Median Absolute Deviation (MAD)0.178746173
Skewness0.640019937
Sum136.1248847
Variance0.06059072485
MonotonicityNot monotonic
2021-05-25T18:23:30.998325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.61940857832
 
0.4%
02
 
0.4%
0.67799139492
 
0.4%
0.45959305571
 
0.2%
1.0175044931
 
0.2%
0.78845509651
 
0.2%
0.55136574171
 
0.2%
0.15292813291
 
0.2%
0.4630741521
 
0.2%
0.31342990511
 
0.2%
Other values (400)400
82.8%
(Missing)70
 
14.5%
ValueCountFrequency (%)
-0.088691109071
0.2%
02
0.4%
5.537123642 × 10-51
0.2%
0.00012019375421
0.2%
0.000364557231
0.2%
0.00037243509361
0.2%
0.00038492761111
0.2%
0.0004475821271
0.2%
0.00047791253321
0.2%
0.00073383176051
0.2%
ValueCountFrequency (%)
1.0794950811
0.2%
1.071434181
0.2%
1.0384712491
0.2%
1.0175044931
0.2%
1.0150260331
0.2%
1.0141520941
0.2%
0.91689795111
0.2%
0.88958897091
0.2%
0.87313528971
0.2%
0.87294118081
0.2%

Interactions

2021-05-25T18:22:17.250234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:17.709046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:18.038164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:18.413160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:18.792147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:19.123261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:19.483299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:19.778509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:20.059757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:20.342002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:20.630230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:20.947383image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:21.244588image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:21.548354image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:21.830599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:22.142765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:22.441965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:22.752134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:23.049340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:23.394417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:23.852212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:24.149418image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:24.480533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:24.778734image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:25.100872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:25.399075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:25.731187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:26.247835image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:26.698084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:27.123272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:27.543148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:28.016484image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:28.419586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:28.861403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:29.258343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:29.611398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:29.956474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:30.285598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:30.625684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:30.932445image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:31.351326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:31.685433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:32.077383image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:32.413485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:32.791474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:33.115776image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:33.445892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:33.789973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:34.115103image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:34.486110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:34.806255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:35.167290image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-05-25T18:22:38.274979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:38.586145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:38.991063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-05-25T18:22:40.901014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:41.287980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:41.636647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:42.076606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-05-25T18:22:42.825603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:43.175666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:43.495812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:43.766088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:44.034369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:44.438803image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:44.827775image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:45.105548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:45.711435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:46.040756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-25T18:22:46.360900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-05-25T18:23:31.304503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-25T18:23:31.910918image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-25T18:23:32.547738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-25T18:23:33.255884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-25T18:23:34.055094image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-25T18:22:47.543737image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-25T18:22:52.181535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-05-25T18:22:54.382647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-05-25T18:22:56.267243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

REGIÃOUFENTEANON.JURPOPTAM.ATVAD.PROGC.PROGPRT_massaMassaSeg.GR_PODGR_SEGRGR_C.ÉTCIE_RELCIE_FISCCIE_AUDCIE_CRPGI_DIV.CARTGI_C.TÉCGI_P.INVGI_COMITÊGI_CONSSA_AMRTZSA_PUB.RELSA_REPASSA_R.ALIQGTI_POL.INFR.GTI_INV.TIDDM_CARTDDM_ACESDDM_ATASRP_REG.ENVRP_REL.GOVRP_SITEPS_OUVPS_GESTIP_REP.CFIP_SELECLI_CERT.MBLI_DIR.CERTLI_P.EST.R.FININD.SFR.INVVAR.INVR.ATIND.COB
01COGOAbadia de Goiás1998-03-26Autarquia8,958.0015,368,522.77NÃONaNPEQUENO PORTEmais favorável11.000.000REGREGIRGREGB011.000IRGREG1.0000.001.00REG0.00A0.000.001.001.00BELC10.001,023,035.101.662,649,065.320.21-30,673,267.790.33
12COGOAbadiânia2001-10-10Autarquia20,461.00174,719.81NÃONaNPEQUENO PORTEmais favorável11.000.000REGREGJUDREGC101.000JUDREG1.0000.000.00REG0.00B0.000.001.000.00CELC10.00-1,570,789.260.53-197,592.51-0.53NaNNaN
23NECEAcarapé2013-04-26Autarquia15,036.0043,696.21NÃONaNPEQUENO PORTEmais favorável10.000.000REGREGIRGREGC101.000REGIRG1.0000.001.00REG0.00B0.001.000.000.00BESCC10.00-559,378.130.84-7,724.83-0.15-91,387,699.630.00
34NECEAcopiara2009-08-12Autarquia54,481.0045,910,781.05NÃONaNMÉDIO PORTEmais favorável11.000.000REGREGREGREGB111.000REGREG1.0001.001.00REG1.00A1.001.001.000.00AESCC10.008,183,757.083.215,722,056.290.14-123,089,791.110.27
45COGOAcreúna2005-08-22Autarquia22,546.0042,459,373.90NÃONaNMÉDIO PORTEmenos favorável11.000.000REGREGREGREGB111.001REGREG1.0000.001.00REG1.00A1.001.001.001.00AELC10.006,020,421.461.825,352,032.170.14-131,203,244.980.24
56SPRAdrianópolis1994-07-29Autarquia5,857.008,010,256.66NÃONaNPEQUENO PORTEmenos favorável11.000.000REGREGREGREGA110.000REGREG0.0000.001.00REG0.00A0.001.000.001.00CESCB00.00NaN0.002,029,296.800.34-43,263,463.210.16
67NEPEAfogados da Ingazeira2002-08-01Autarquia37,404.00405,891.32NÃONaNMÉDIO PORTEmenos favorável11.000.000JUDREGJUDREGB110.001JUDJUD0.0000.001.00REG0.00C0.001.000.000.00BESCC00.00-5,075,837.070.60307,209.403.11-122,786,505.770.00
78NEPEAfrânio2001-03-28Órgão Adm. Direta19,810.0028,316,185.30NÃONaNPEQUENO PORTEmais favorável11.000.000REGREGJUDREGB110.000REGREG1.0000.001.00REG0.00A0.001.000.000.00AESCC00.002,875,091.882.303,712,963.120.15-34,345,138.060.45
89NPAAfuá2002-06-21NaN39,567.002,592,301.75NÃONaNMÉDIO PORTEmais favorável10.000.000IRGREGIRGIRGB110.000IRGIRG0.0000.000.00REG0.00CNaN1.000.000.00NaNESCC00.00-757,945.770.61-813,289.43-0.24NaNNaN
910NEPEAgrestina2013-06-27Autarquia25,065.004,430,202.46NÃONaNMÉDIO PORTEmais favorável10.000.000REGREGJUDREGA111.001JUDREG1.0000.000.00REGNaNA0.001.000.000.00CESCC10.00823,682.471.221,095,091.570.33-89,869,362.100.05

Last rows

REGIÃOUFENTEANON.JURPOPTAM.ATVAD.PROGC.PROGPRT_massaMassaSeg.GR_PODGR_SEGRGR_C.ÉTCIE_RELCIE_FISCCIE_AUDCIE_CRPGI_DIV.CARTGI_C.TÉCGI_P.INVGI_COMITÊGI_CONSSA_AMRTZSA_PUB.RELSA_REPASSA_R.ALIQGTI_POL.INFR.GTI_INV.TIDDM_CARTDDM_ACESDDM_ATASRP_REG.ENVRP_REL.GOVRP_SITEPS_OUVPS_GESTIP_REP.CFIP_SELECLI_CERT.MBLI_DIR.CERTLI_P.EST.R.FININD.SFR.INVVAR.INVR.ATIND.COB
473474SESPPotirendaba2005-07-14Autarquia17,516.0028,626,302.33NÃONaNMÉDIO PORTEmenos favorável11.000.000REGREGIRGREGNaN101.000REGREG0.0000.001.00REG1.00A0.001.001.001.00AESCC10.00-1,264,529.950.774,627,248.420.19-85,069,649.350.25
474475SPRQuatro Barras1992-06-02Autarquia23,911.0064,916,581.30NÃONaNMÉDIO PORTEmais favorável11.000.000REGREGREGREGB111.000REGREG0.0000.001.00REG0.00B0.001.001.001.00AESCB10.0011,021,157.653.0610,214,557.450.19-46,980,926.290.58
475476NEPEQuixaba2005-11-22Autarquia1,983.009,198,354.43NÃONaNPEQUENO PORTEmenos favorável11.000.000REGREGREGREGNaN000.000JUDREG0.0000.001.00REG1.00C1.001.001.001.00AESCC00.00576,304.351.261,570,096.790.21-35,937,542.780.20
476477SPRReserva do Iguaçu2001-11-28Autarquia8,069.0027,065,731.84NÃONaNPEQUENO PORTEmais favorável11.000.000REGREGIRGREGNaN100.000REGIRG0.0000.001.00REG0.00B0.001.001.001.00BELC00.003,865,829.143.144,290,810.450.19-8,478,942.790.76
477478SEMGRio Acima2002-12-26Autarquia10,420.002,495,332.62NÃONaNPEQUENO PORTEmais favorável11.000.000REGREGIRGREGB111.000IRGIRG0.0000.001.00REG1.00B0.001.001.001.00CELC10.001,081,186.701.35786,082.670.46NaNNaN
478479COMTRio Branco2006-01-04Fund. Dir. Púbico413,418.0015,543,271.78NÃONaNPEQUENO PORTEmenos favorável11.000.000REGREGREGREGA110.000IRGREG1.0000.001.00REG0.00A0.000.001.001.00AESCC10.0091,572,003.133.2390,215,306.370.23-295,511,611.220.62
479480SESPRubinéia2002-02-28Autarquia3,170.0027,215,102.96NÃONaNPEQUENO PORTEmenos favorável11.000.000REGREGREGREGA111.001REGREG1.0001.001.00REG1.00A0.001.001.001.00CESCB00.00922,542.931.423,948,486.530.17-22,144,334.710.55
480481SEMGSabinópolis2006-08-29Autarquia15,416.0023,300,846.96NÃONaNPEQUENO PORTEmenos favorável11.000.000REGREGREGREGB111.000IRGREG1.0001.001.00REG0.00B0.001.001.001.00AESCC10.002,978,677.971.603,471,397.210.18-46,424,138.900.33
481482SESPSanta Rita do Passa Quatro2004-10-14Autarquia27,600.00143,121,440.04NÃONaNMÉDIO PORTEmenos favorável11.000.000REGREGREGREGB111.001REGREG1.0000.001.00REG1.00C1.001.001.001.00BESCC10.00-1,519,645.240.8321,704,440.440.18-30,460,061.140.82
482483SRSSanta Rosa2001-01-29Autarquia73,575.00258,269,174.68NÃONaNMÉDIO PORTEmenos favorável11.000.000REGREGREGREGA110.000REGREG0.0001.001.00REG1.00A0.001.001.001.00BESCC10.006,720,887.521.2428,266,671.950.12-341,449,342.360.43